{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "from scipy import signal\n",
    "import cmath\n",
    "nsamples = 4_096\n",
    "fs = 44_100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_signal(samples = 2048, fs = 44_100):\n",
    "    f0, f1, f2, f3, f4 = 1_000, 4_000, 6_000, 8_000, 17_357\n",
    "    ts = 1 / fs\n",
    "    time = np.linspace(0, samples*ts, samples)\n",
    "\n",
    "    tone_0 = 1 * np.sin(2*np.pi*f0*time)\n",
    "    tone_1 = 0.8 * np.sin(2*np.pi*f1*time)\n",
    "    tone_2 = 2 * np.sin(2*np.pi*f2*time)\n",
    "    tone_3 = 0.1 * np.sin(2*np.pi*f3*time)\n",
    "    tone_4 = 0.3* np.sin(2*np.pi*f4*time)\n",
    "    signal = tone_0 + tone_1 + tone_2*1j + tone_3*1j + tone_4*1j\n",
    "    return signal\n",
    "\n",
    "def compute_fft(data, fs = 44_100):\n",
    "    samples = len(data)\n",
    "    ts = 1 / fs\n",
    "    spectrum = np.fft.fft(data)\n",
    "    freqs = np.fft.fftshift(np.fft.fftfreq(samples, d=ts))\n",
    "    return spectrum, freqs\n",
    "\n",
    "def init(n):\n",
    "    omg = [cmath.exp(-2j * cmath.pi * i / n) for i in range(n)]\n",
    "    return omg\n",
    "\n",
    "def fft(x):\n",
    "    a = np.copy(x)\n",
    "    n = len(a)\n",
    "    omg = init(n)\n",
    "\n",
    "    lim = 0\n",
    "    while (1 << lim) < n:\n",
    "        lim += 1\n",
    "\n",
    "    for i in range(n):\n",
    "        t = 0\n",
    "        for j in range(lim):\n",
    "            if ((i >> j) & 1):\n",
    "                t |= (1 << (lim - j - 1))\n",
    "        if i < t:\n",
    "            a[i], a[t] = a[t], a[i]  # i < t 的限制使得每对点只被交换一次（否则交换两次相当于没交换）\n",
    "    # print(\"swap\")\n",
    "    # print(a[:8])\n",
    "\n",
    "    l = 2\n",
    "    while l <= n:   # stage循环 l为该stage子序列的长度\n",
    "        m = l // 2  # 根据FFT特性，后一半的数值可由前一半的中间值计算得出\n",
    "        for p in range(0, n, l):\n",
    "            for i in range(m):\n",
    "                t = omg[n // l * i] * a[p + i + m]\n",
    "                # print('t=',t)\n",
    "                a[p + i + m] = a[p + i] - t\n",
    "                a[p + i] += t\n",
    "        l *= 2\n",
    "        \n",
    "    return a\n",
    "\n",
    "def fft_4k(signal):\n",
    "    xx=[signal[i::4] for i in range(4)]\n",
    "    x=[fft(xx[i]) for i in range(4)]\n",
    "    for ii in range(4):\n",
    "        for jj in range(1024):\n",
    "            x[ii][jj]*=cmath.exp(-2j * cmath.pi * ii*jj / nsamples)\n",
    "    a=[]\n",
    "    for i in range(1024):\n",
    "        res=fft(np.array([x[j][i] for j in range(4)]))\n",
    "        a+=list(res)\n",
    "        if i<4:\n",
    "            print(res)\n",
    "    b=[a[i::4] for i in range(4)]\n",
    "    a=[]\n",
    "    for ii in range(4):\n",
    "        a+=b[ii]\n",
    "    return np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4.23345774+3.70125613j -0.82840011-3.13506168j  0.32847814-2.28770035j\n",
      "  0.97928979-1.9484531j ]\n",
      "[ 4.23601699+3.74725322j -0.8253336 -3.12828299j  0.32894422-2.28727172j\n",
      "  0.98067108-1.95325187j]\n",
      "[ 4.23890819+3.79330897j -0.82227969-3.1215301j   0.32941003-2.2868538j\n",
      "  0.98205895-1.95807279j]\n",
      "[ 4.242132  +3.83945376j -0.81923828-3.1148028j   0.32987557-2.28644658j\n",
      "  0.98345348-1.96291603j]\n",
      "[4.23345774+3.70125613j 4.23601699+3.74725322j 4.23890819+3.79330897j ...\n",
      " 4.22777126+3.56331359j 4.2293345 +3.60931667j 4.23123019+3.65528739j]\n"
     ]
    }
   ],
   "source": [
    "signal = generate_signal(nsamples, fs)\n",
    "spectrum, freqs = compute_fft(signal, fs)\n",
    "# spectrum_btf = fft(signal)\n",
    "spectrum_btf=fft_4k(signal)\n",
    "print(spectrum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dyx/.local/lib/python3.8/site-packages/matplotlib/cbook/__init__.py:1335: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return np.asarray(x, float)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f781ee904f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum), label=\"Scipy\")\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum_btf), label=\"Btf\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vector2file_cint16(data: np.array, file: str, plio = 128, bits = 16, scale: bool=True):\n",
    "    \"\"\"\n",
    "    data: samples\n",
    "    file: output filename\n",
    "    plio: bit width of the PLIO port\n",
    "    bits: bit precision per sample\n",
    "    \"\"\"\n",
    "    \"\"\"Scale signal to use full precision\"\"\"\n",
    "    maxr = np.max(np.abs(data.real))\n",
    "    maxi = np.max(np.abs(data.imag))\n",
    "    maxv = maxr if maxr > maxi else maxi\n",
    "    if scale:\n",
    "        # vscale = 2**int(np.floor(np.log2((1 << (bits-1)) / maxv)))\n",
    "        vscale = 64\n",
    "    else:\n",
    "        vscale = 1\n",
    "    data = data * (vscale if scale else 1)\n",
    "    \"\"\"Write value to file\"\"\"\n",
    "    with open(file, 'w', newline='') as f:\n",
    "        for i, v in enumerate(data):\n",
    "            r = np.int16(v.real)\n",
    "            c = np.int16(v.imag)\n",
    "            f.write(\"{} {} \".format(r, c))\n",
    "            if (((i+1) % 4) == 0 and plio == 128) or (((i+1) % 2) == 1 and plio == 64):\n",
    "                f.write('\\n')\n",
    "    \n",
    "    return vscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64\n"
     ]
    }
   ],
   "source": [
    "for i in range(4): \n",
    "    vscale=vector2file_cint16(signal[i::4], 'DataInFFT'+str(i)+'.txt', scale=True)\n",
    "print(vscale)\n",
    "# vector2file_cint16(signal, 'DataInFIR0.txt', scale=True)\n",
    "# vector2file_cint16(spectrum, 'DataOutFFT0.txt', scale=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WAIT! ! ! AIE! ! !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "def read_file(file: str, samples: int):\n",
    "    value = np.zeros(samples, dtype=np.complex128)\n",
    "    count = 0\n",
    "    with open(file,'r') as f:\n",
    "        reader = csv.reader(f, delimiter=\" \")\n",
    "        for line in reader:\n",
    "            if 'T' in line[0]:\n",
    "                continue\n",
    "            for i in range(0, len(line), 2):\n",
    "                if line[i] != '':\n",
    "                    value[count] = np.int16(line[i]) + 1j* np.int16(line[i+1])\n",
    "                    count = count + 1\n",
    "    return value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the output file from the AIE, now using SciPy file for simplicity\n",
    "signal_read0 = read_file('DataOutFFT0.txt', nsamples//4)\n",
    "signal_read1 = read_file('DataOutFFT1.txt', nsamples//4)\n",
    "signal_read2 = read_file('DataOutFFT2.txt', nsamples//4)\n",
    "signal_read3 = read_file('DataOutFFT3.txt', nsamples//4)\n",
    "signal_read = np.concatenate([signal_read0, signal_read1, signal_read2, signal_read3])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f781c678820>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scaled_in = read_file('DataInFFT0.txt', nsamples)\n",
    "# spectrum, _ = compute_fft(scaled_in,fs)\n",
    "\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(freqs / 1e3, 20*np.log(spectrum), label=\"Scipy\")\n",
    "plt.plot(freqs / 1e3, 20*np.log(signal_read/vscale), label=\"AIE\")\n",
    "#plt.plot(freqs / 1e3, 20*np.log(signal_read), label=\"AIE\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array_equal(spectrum, signal_read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, ...,  True,  True,  True])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isclose(spectrum, signal_read, 2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = spectrum - signal_read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29819.345408104928"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.abs(error.real))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31623.471803758523"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(np.abs(error.imag))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numpy: Max Real 1184.325379618681, Max Imag 2925.1613477124156\n",
      "Numpy: Max Real 29820.0, Max Imag 32128.0\n"
     ]
    }
   ],
   "source": [
    "def findmax(fftnp, fftaie):\n",
    "    print(f'Numpy: Max Real {np.max(np.abs(fftnp.real))}, Max Imag {np.max(np.abs(fftnp.imag))}')\n",
    "    print(f'Numpy: Max Real {np.max(np.abs(fftaie.real))}, Max Imag {np.max(np.abs(fftaie.imag))}')\n",
    "\n",
    "findmax(spectrum, signal_read)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "bd76463b23fe99089a42dcbd61c93a219c6242df3d4b53db5f5a167e07e18f3c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
